Model building is a key step in a data mining (DM) task in machine learning (ML). For example, in a universal parallel framework (Spark), during modeling, a master node (Master) may deliver a task to a plurality of slave nodes (Slave) for execution. Usually, a plurality of rounds of iterative computation needs to be implemented during task execution. After each round of iterative computation ends, each slave node needs to report an iterative computation result to the master node, the master node updates a model and delivers an updated parameter to the slave node, and the slave node starts to execute a next round of iterative computation.
Therefore, the master node needs to perform model updating and parameter delivering for a plurality of times. In a large-scale training scenario, a workload of the master node is relatively heavy. As a result, the master node tends to become a bottleneck of the entire training scenario.